Decision Making and Rationality – Part 1

First let me say thanks to all of you who participated in my most recent survey. The results are in and I’ll be sharing the data and my interpretation of the data over the next four posts.

I’m fascinated by the process people go through to make decisions and that’s what my survey was attempting to get at. I’ve enjoyed Dan Ariely’s books, Predictably Irrational and The Upside of Irrationality, and his work ties into much of what I’ll be sharing. Another very interesting book on this subject is William Poundstone’s Priceless: The Myth of Fair Value (and How to Take Advantage of It). All three books had a profound impact on my thinking in this area so I decided to see if what I’ve read about would bear out in the real world with my readers.

Before we begin, let me put out this disclaimer: I’m not a social scientist or behavioral economist. This was not a rigorous scientific study, just my attempt to see how people would respond to certain scenarios so I could see how the responses correlated to things I’ve learned over the years. I also need to tell you I’m not a professional surveyor either. I’m learning as I go and point this out because I had a few people contact me because they had issues with certain questions. Sorry if a question or two rubbed you the wrong way but thanks for participating and for taking the time to reach out to me.

The Surveys I asked people to take one of two surveys based on the letter their last name started with. There was no psychology to this. My only goal was to get an even, random split between the two surveys and I accomplished that. As I share the questions you’ll see both surveys were very similar but with slight twists on each question and those twists will be the points of comparison when it comes to decision making. So without further adieu let’s get started.

Question 1 asked the sex of the participant because I was interested to see if there were any significant differences in the answers given by each gender. In case you’re interested, 58% of the people taking Survey A were male and 42% were female. On Survey B it was a 50-50 split which meant the overall split for all participants was 54% male and 46% female.

Question 2 on Survey A people were asked to enter their four-digit birth year while Survey B had people put in their two-digit birth year. That question was only to prime you because many different studies show that mere exposure to words or numbers can change people’s responses and behaviors and I wanted to see if that was the case with those who took my survey when they answered question 3.

In case you’re curious, most people who took the surveys were in their mid-40s. On Survey A the average birth year was close to 1964 and on Survey B the average was 1966.

Question 3 asked, “If you could get paid what you really believe you’re worth (not what you’d love to earn) what annual salary would you ask for?”

Priming would lead me to believe people who entered a four-digit birth year, like 1963, would be subtly influenced to put down a higher salary than those who entered a two-digit year like 63. With 100 responses for each survey those who entered a four-digit birth year thought they were worth $147,413, whereas those who put in a two-digit birth year said they’d ask for $142,775.

I doubt the $4638 spread, a 3.2% difference, is statistically significant. However, what seemed to have influence was the male-female ratio because generally women would ask for a lot less on the salary. The average salary entered by women was $126,005 vs. $161,644 for men. In other words, the men thought they should get 28% more than the women! The average birth year was 1965 for both men and women so it would be hard to explain the difference based on eligible years in the workforce.

Maybe unknowingly the real priming was having participants enter their sex at the start of the survey. I say that because there’s lots of interesting data that shows entering sex or race can impact performance on things like tests. In Asia, entering gender tends lead to lower test scores for women whereas in the U.S., African-Americans scored lower on tests when they had to enter their race. To learn more about that I’ll refer you to the work cited in Malcolm Gladwell’s best seller, Blink.

Here’s the point: What you’re exposed to first can make a big difference in your thinking – good or bad. The first number a realtor or car salesman puts out can have a significant impact on what you ultimately pay. It’s a form of priming called anchoring. Your best defense might be having a firm number (monthly or total) for that dream house or car that you won’t deviate from. And when it comes to race, sex, religion and other factors we’d all do well to understand the preconceived ideas we hold because we might unknowingly be negatively influencing ourselves.

Brian, CMCT
influencepeople
Helping You Learn to Hear “Yes”.